Opportunity and AI fit

Review questions

Opportunity and AI fit — Integrated summary

Begin with a user job, baseline, and non-AI alternative. Value error costs and adoption, then allocate discovery funds using falsifiable evidence. Frame a user job, baseline and non-ai alternative before proposing a model. Value the opportunity with error costs, adoption and counterfactual baseline. Design a value hypothesis and proof threshold. Allocate discovery investment across an ai opportunity portfolio.

Review questions

Test your understanding with these questions. Marking and saved results are available with a free account.

  1. Question 1What defensible product step comes before buying a routing classifier?
  2. Question 2What is the conditional weekly operating value?
  3. Question 3Which judgment separates adoption from product value?
  4. Question 4Which plan respects money, capacity and dependency?
  5. Question 5What reliable count of unique labeled examples is available now?
  6. Question 6What first capability test best fits this job?
  7. Question 7What are recall for required cases and total review requests?
  8. Question 8Which control should reject this case while accepting a matching current version?
  9. Question 9Which design assigns decision authority precisely?
  10. Question 10What interface correction is most appropriate?
  11. Question 11What follows before declaring the incident over?
  12. Question 12What is the most useful initial diagnosis?
  13. Question 13What is the average variable cost per case?
  14. Question 14What is the disciplined conclusion?
  15. Question 15What is the release-gate verdict?
  16. Question 16What risk should be priced and constrained in the contract?
  17. Question 17What should the launch lead do?
  18. Question 18Which hypothesis should be tested first?
  19. Question 19What is the first disciplined decision?
  20. Question 20What is a sound interim decision?